Data heterogeneity means the features contained in the collected data are of different types (e.g., “glucose in urine” is numerical while “marriage” is categorical) and distributions (e.g., “gender” tends to be evenly distributed while “occupation” normally follows a long-tail distribution).
Although deep neural networks (DNNs) have shown promising performance in vision and language domains [1]}, it is much harder to train DNNs with mixture types of input [2]}, especially when the input distribution is unstable [3]}.
In contrast to DNNs, decision trees [4]} are insensitive to data types and distributions, and thus it is appealing to deal with heterogeneous data using decision trees [4]}, [6]}.
More importantly, tree-based methods can implicitly handle the problem of features missing, which is common in medical follow-up data submitted by users, as we will mention in Section .
Beyond that, decision trees are also easy to visualization and further interpreted [7]}, [8]} as we mention in Appendix , which is another superior advantage over neural networks.
Therefore, in this paper, to handle the data heterogeneity challenge in diabetes prediction, we construct our method based on the gradient boosting decision trees (GBDT) [9]}, which is one of the most popular and powerful tree algorithms.
